EmptyDroplets (FDR <= 0.1) + scDblFindersetwd("/media/jacopo/Elements/re_align/MM/PRJNA732205/SAMN19314091/SRR14629353/")
# Load the libraries (from Sarah script + biomart)
library(tidyverse) # packages for data wrangling, visualization etc
library(Seurat) # scRNA-Seq analysis package
library(clustree) # plot of clustering tree
library(ggsignif) # Enrich your 'ggplots' with group-wise comparisons
library(clusterProfiler) #The package implements methods to analyze and visualize functional profiles of gene and gene clusters.
library(org.Hs.eg.db) # Human annotation package neede for clusterProfiler
library(ggrepel) # extra geoms for ggplo2
library(patchwork) #multiplots
library(reticulate)
Load and do the QC for the cellranger data
#list.files(".")
dat <- Read10X(data.dir ="./out/counts_filtered/")
dat <- CreateSeuratObject(dat) # Create the seurat object from the 10x data
kb.initial <- dat@assays[["RNA"]]@counts@Dim[[2]]
cat("Initial number of cells:", kb.initial,
"\nNumber of genes:", dat@assays[["RNA"]]@counts@Dim[[1]])
## Initial number of cells: 6336
## Number of genes: 36601
Empty cells were already filtered, check for % mt RNA and death markers:
# first calculate the mitochondrial percentage for each cell
dat$percent_mt <- PercentageFeatureSet(dat, pattern="^MT.")
# make violin plots
mt_rna = 3
max_counts = 50000
# Check some feature-feature relationships
# % mt RNA vs n Counts, n Features vs n Counts
# Check some feature-feature relationships
# % mt RNA vs n Counts, n Features vs n Counts
VlnPlot(dat, features = c("nCount_RNA", "nFeature_RNA", "percent_mt")) + geom_hline(yintercept=mt_rna, linetype = "dotted")
plot1 <- FeatureScatter(dat, feature1 = "nCount_RNA", feature2 = "percent_mt")
plot1 <- plot1 + geom_hline(yintercept=mt_rna, linetype = "dotted")
plot2 <- FeatureScatter(dat, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot2 <- plot2 + geom_vline(xintercept = max_counts, linetype = "dotted")
plot1
plot2
## cells retained by mt RNA content ( 3 %): 5665
## percentage of retained cells: 89.41 %
## cells retained by counts ( 50000 ): 5654
## percentage of retained cells: 89.24 %
Check the distribution of the cells with low counts and control death markers:
min_counts = 250
hist(dat@meta.data$nCount_RNA, breaks = 100, xlab = "Counts")
hist(dat@meta.data$nCount_RNA, breaks = 1000, xlab = "Counts", xlim = c(0,5000))
hist(dat@meta.data$nCount_RNA, breaks = 10000, xlab = "Counts", xlim = c(0,1000))
abline(v=min_counts, col="red", lty = 3)
The evident peak of cells with < 200 counts could contain dying
cells.
# Subset the dataset to focus only on those cells with low counts
dat.lowcount <- subset(dat, subset = nCount_RNA < min_counts)
# Get the mean of the counts for each gene and sort them decreasing
meanCounts <- rowMeans(GetAssayData(object = dat.lowcount, slot = 'counts'))
meanCounts <- sort(meanCounts, decreasing = T)
# A boxplot can help to observe the distribution of the means
#boxplot(meanCounts)
# Print the most highly expressed genes
head(meanCounts, 30)
## IGLC2 HBB IGHG1 IGHG3 IGKC HBA2 IGHGP
## 47.8337875 5.9536785 5.4059946 5.1771117 4.5449591 3.3106267 2.9128065
## B2M MALAT1 IGHA1 IGLC3 IGLV6-57 SSR4 IGHV3-73
## 2.4686649 1.8474114 1.4223433 1.3814714 1.3051771 1.0517711 0.8147139
## JCHAIN IGLC1 HBA1 RPLP1 MZB1 TMSB4X HLA-B
## 0.6675749 0.6103542 0.5967302 0.5940054 0.5286104 0.4877384 0.4713896
## RPL41 RPL10 EIF1 FTL RPL13 RPS14 RPS18
## 0.4495913 0.4277929 0.4005450 0.3405995 0.3351499 0.3242507 0.3133515
## RPL13A RPL21
## 0.2888283 0.2724796
## cells retained by counts ( 250 ): 5287
## percentage of retained cells: 83.44 %
dir.create("result")
saveRDS(dat, file = "./result/SAMN19314091_clean_QC.Rds")
#Normalize
dat <- NormalizeData(dat)
# Find the first 4000 variabe features
dat <- FindVariableFeatures(dat, selection.method = "vst", nfeatures = 4000)
Set mean expression to 0 and variance across 1 to avoid highly expressed genes drive the forwarding analyses. Since negative expression is meaningless, scaled data are useful only for UMAP and clustering
# scale data, the scaled data are saved in:
# dat[["RNA"]]@scale.data
all.genes <- rownames(dat)
dat <- ScaleData(dat, vars.to.regress = c("percent_mt","nCount_RNA"))
dat <- RunPCA(dat, features = VariableFeatures(object = dat), verbose = F, seed.use = 1)
print(dat[["pca"]], dims = 1:5, nfeatures = 5)
## PC_ 1
## Positive: HBA1, HBA2, HBB, SLC25A37, ALAS2
## Negative: RPL10, RPL13A, RPS14, EEF1A1, RPS18
## PC_ 2
## Positive: MZB1, IGLC3, IGHG2, FKBP11, SEC11C
## Negative: HBA1, HBA2, HBB, LY6E, CD52
## PC_ 3
## Positive: RPL13A, RPL34, RPS3A, RPS23, RPS18
## Negative: HLA-B, GADD45B, JUNB, LDHA, H3F3B
## PC_ 4
## Positive: ITM2C, MZB1, IGHA1, DERL3, FKBP11
## Negative: HBA1, HBA2, HBB, LINC01781, SLC25A37
## PC_ 5
## Positive: IGKC, GADD45B, CYTOR, HSPA1B, MCL1
## Negative: IGLC3, QPCT, CCND3, SEC11C, COMMD3
UMAP is a graph-based method of clustering. The first step in this process is to construct a KNN graph based on the euclidean distance in PCA space:
dat <- FindNeighbors(dat, dims = 1:20)
The graph now can be used as input for the function
runUMAP()
dat <- RunUMAP(dat, dims = 1:20, seed.use = 1)
DimPlot(dat, reduction = 'umap', seed = 1)
## QC metrics
## markers